Constructing scales Re-coding pusing reshaping data (4field)
centering data within and between
Modelling
# For indistinguishable Dyads
model_rows_fixed <- c(
'Intercept',
# '-- WITHIN PERSON MAIN EFFECTS --',
'persuasion_self_cw',
'persuasion_partner_cw',
'pressure_self_cw',
'pressure_partner_cw',
'pushing_self_cw',
'pushing_partner_cw',
'day',
'weartime_self_cw',
# '-- BETWEEN PERSON MAIN EFFECTS',
'persuasion_self_cb',
'persuasion_partner_cb',
'pressure_self_cb',
'pressure_partner_cb',
'pushing_self_cb',
'pushing_partner_cb',
'weartime_self_cb'
)
model_rows_fixed_ordinal <- c(
model_rows_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rows_fixed[2:length(model_rows_fixed)]
)
model_rows_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(persuasion_self_cw)',
'sd(persuasion_partner_cw)',
'sd(pressure_self_cw)',
'sd(pressure_partner_cw)',
'sd(pushing_self_cw)',
'sd(pushing_partner_cw)',
# '-- CORRELATION STRUCTURE -- ',
'ar[1]',
'ma[1]',
'cosy',
'nu',
'shape',
'sderr',
'sigma'
)
model_rows_random_ordinal <- c(model_rows_random,'disc')
# For indistinguishable Dyads
model_rownames_fixed <- c(
'Intercept',
# '-- WITHIN PERSON MAIN EFFECTS --',
'Daily received persuasion target -> target',
'Daily received persuasion target -> agent',
'Daily received pressure target -> target',
'Daily received pressure target -> agent',
'Daily received pushing target -> target',
'Daily received pushing target -> agent',
'Day',
'Daily weartime',
# '-- BETWEEN PERSON MAIN EFFECTS',
'Mean received persuasion target -> target',
'Mean received persuasion target -> agent',
'Mean received pressure target -> target',
'Mean received pressure target -> agent',
'Mean received pushing target -> target',
'Mean received pushing target -> agent',
'Mean weartime'
)
model_rownames_fixed_ordinal <- c(
model_rownames_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rownames_fixed[2:length(model_rownames_fixed)]
)
model_rownames_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(Daily received persuasion target -> target)',
'sd(Daily received persuasion target -> agent)',
'sd(Daily received pressure target -> target)',
'sd(Daily received pressure target -> agent)',
'sd(Daily received pushing target -> target)',
'sd(Daily received pushing target -> agent)',
# '-- CORRELATION STRUCTURE -- ',
'ar[1]',
'ma[1]',
'cosy',
'nu',
'shape',
'sderr',
'sigma'
)
model_rownames_random_ordinal <- c(model_rownames_random,'disc')
# For indistinguishable Dyads
model_rownames_fixed <- c(
"Intercept",
# "-- WITHIN PERSON MAIN EFFECTS --",
"Daily persuasion experienced",
"Daily persuasion utilized (partner's view)", # OR partner received
"Daily pressure experienced",
"Daily pressure utilized (partner's view)",
"Daily pushing experienced",
"Daily pushing utilized (partner's view)",
"Day",
"Daily weartime",
# "-- BETWEEN PERSON MAIN EFFECTS",
"Mean persuasion experienced",
"Mean persuasion utilized (partner's view)",
"Mean pressure experienced",
"Mean pressure utilized (partner's view)",
"Mean pushing experienced",
"Mean pushing utilized (partner's view)",
"Mean weartime"
)
model_rownames_fixed_ordinal <- c(
model_rownames_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rownames_fixed[2:length(model_rownames_fixed)]
)
model_rownames_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
"sd(Daily persuasion experienced)",
"sd(Daily persuasion utilized (partner's view))", # OR partner received
"sd(Daily pressure experienced)",
"sd(Daily pressure utilized (partner's view))",
"sd(Daily pushing experienced)",
"sd(Daily pushing utilized (partner's view))",
# '-- CORRELATION STRUCTURE -- ',
'ar[1]',
'ma[1]',
'cosy',
'nu',
'shape',
'sderr',
'sigma'
)
model_rownames_random_ordinal <- c(model_rownames_random,'disc')
rows_to_pack <- list(
"Within-Person Effects" = c(2,9),
"Between-Person Effects" = c(10,16),
"Random Effects" = c(17, 23),
"Additional Parameters" = c(24,30)
)
rows_to_pack_ordinal <- list(
"Intercepts" = c(1,6),
"Within-Person Effects" = c(2+5,9+5),
"Between-Person Effects" = c(10+5,16+5),
"Random Effects" = c(17+5, 23+5),
"Additional Parameters" = c(24+5,30+6)
)
HURDLE MODELS
# For indistinguishable Dyads
model_rows_fixed_hu <- c(
'Intercept',
'hu_Intercept',
# '-- WITHIN PERSON MAIN EFFECTS --',
'persuasion_self_cw',
'persuasion_partner_cw',
'pressure_self_cw',
'pressure_partner_cw',
'pushing_self_cw',
'pushing_partner_cw',
'day',
'weartime_self_cw',
# '-- BETWEEN PERSON MAIN EFFECTS',
'persuasion_self_cb',
'persuasion_partner_cb',
'pressure_self_cb',
'pressure_partner_cb',
'pushing_self_cb',
'pushing_partner_cb',
'weartime_self_cb',
# HURDLE MODEL
# '-- WITHIN PERSON MAIN EFFECTS --',
'hu_persuasion_self_cw',
'hu_persuasion_partner_cw',
'hu_pressure_self_cw',
'hu_pressure_partner_cw',
'hu_pushing_self_cw',
'hu_pushing_partner_cw',
'hu_day',
'hu_weartime_self_cw',
# '-- BETWEEN PERSON MAIN EFFECTS',
'hu_persuasion_self_cb',
'hu_persuasion_partner_cb',
'hu_pressure_self_cb',
'hu_pressure_partner_cb',
'hu_pushing_self_cb',
'hu_pushing_partner_cb',
'hu_weartime_self_cb'
)
model_rows_random_hu <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(hu_Intercept)',
'sd(persuasion_self_cw)',
'sd(persuasion_partner_cw)',
'sd(pressure_self_cw)',
'sd(pressure_partner_cw)',
'sd(pushing_self_cw)',
'sd(pushing_partner_cw)',
# HURDLE
'sd(hu_persuasion_self_cw)',
'sd(hu_persuasion_partner_cw)',
'sd(hu_pressure_self_cw)',
'sd(hu_pressure_partner_cw)',
'sd(hu_pushing_self_cw)',
'sd(hu_pushing_partner_cw)',
# '-- CORRELATION STRUCTURE -- ',
'ar[1]',
'ma[1]',
'cosy',
'nu',
'shape',
'sderr',
'sigma'
)
# For indistinguishable Dyads
model_rownames_fixed_hu <- c(
"Intercept",
"Hurdle Intercept",
# "-- WITHIN PERSON MAIN EFFECTS --",
"Daily persuasion experienced",
"Daily persuasion utilized (partner's view)", # OR partner received
"Daily pressure experienced",
"Daily pressure utilized (partner's view)",
"Daily pushing experienced",
"Daily pushing utilized (partner's view)",
"Day",
"Daily weartime",
# "-- BETWEEN PERSON MAIN EFFECTS",
"Mean persuasion experienced",
"Mean persuasion utilized (partner's view)",
"Mean pressure experienced",
"Mean pressure utilized (partner's view)",
"Mean pushing experienced",
"Mean pushing utilized (partner's view)",
"Mean weartime",
# HURDLE
# "-- WITHIN PERSON MAIN EFFECTS --",
"Hu Daily persuasion experienced",
"Hu Daily persuasion utilized (partner's view)", # OR partner received
"Hu Daily pressure experienced",
"Hu Daily pressure utilized (partner's view)",
"Hu Daily pushing experienced",
"Hu Daily pushing utilized (partner's view)",
"Hu Day",
"Hu Daily weartime",
# "-- BETWEEN PERSON MAIN EFFECTS",
"Hu Mean persuasion experienced",
"Hu Mean persuasion utilized (partner's view)",
"Hu Mean pressure experienced",
"Hu Mean pressure utilized (partner's view)",
"Hu Mean pushing experienced",
"Hu Mean pushing utilized (partner's view)",
"Hu Mean weartime"
)
model_rownames_random_hu <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(Hurdle Intercept)',
"sd(Daily persuasion experienced)",
"sd(Daily persuasion utilized (partner's view))", # OR partner received
"sd(Daily pressure experienced)",
"sd(Daily pressure utilized (partner's view))",
"sd(Daily pushing experienced)",
"sd(Daily pushing utilized (partner's view))",
# Hurdle
"sd(Hu Daily persuasion experienced)",
"sd(Hu Daily persuasion utilized (partner's view))", # OR partner received
"sd(Hu Daily pressure experienced)",
"sd(Hu Daily pressure utilized (partner's view))",
"sd(Hu Daily pushing experienced)",
"sd(Hu Daily pushing utilized (partner's view))",
# '-- CORRELATION STRUCTURE -- ',
'ar[1]',
'ma[1]',
'cosy',
'nu',
'shape',
'sderr',
'sigma'
)
rows_to_pack_hu <- list(
"Conditional Within-Person Effects" = c(3,10),
"Conditional Between-Person Effects" = c(11,17),
"Hurdle Within-Person Effects" = c(18,25),
"Hurdle Between-Person Effects" = c(26,32),
"Random Effects" = c(33, 46),
"Additional Parameters" = c(47,53)
)
Subjective MVPA
range(df_double$pa_sub, na.rm = T)
## [1] 0 720
hist(df_double$pa_sub, breaks = 40)

hist(log(df_double$pa_sub+00000000001), breaks = 40)

Hurdle Poisson Model
formula <- bf(
pa_sub ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(1 + persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
hu = ~ persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(1 + persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 1.5)", class = "b")
, brms::set_prior("normal(0, 2.5", class = "b", dpar = "hu")
, brms::set_prior("normal(0, 50)", class = "Intercept") # for non-zero PA
, brms::set_prior("normal(6, 2.5)", class = "Intercept", dpar = 'hu') # hurdle part
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
#, brms::set_prior("normal(10, 10", class = "shape")
#, brms::set_prior("cauchy(0, 2)", class = "sigma", lb = 0)
#, brms::set_prior("cauchy(0, 2)", class = "sderr", lb = 0)
#, autocor_prior
)
brms::validate_prior(
prior1,
formula = formula,
data = df_double,
family = hurdle_poisson()
)
## Warning: Rows containing NAs were excluded from the model.
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
pa_sub <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
#family = brms::hurdle_lognormal(),
#family = brms::hurdle_negbinomial(),
family = brms::hurdle_poisson(),
control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("pa_sub_hu_poisson_NOAR", suffix))
#, file_refit = 'always'
)
## Warning: Rows containing NAs were excluded from the model.
check_brms(pa_sub, log_pp_check = TRUE)
##
## Divergences:
## 0 of 12000 iterations ended with a divergence.
##
## Tree depth:
## 12000 of 12000 iterations saturated the maximum tree depth of 10 (100%).
## Try increasing 'max_treedepth' to avoid saturation.
##
## Energy:
## E-BFMI indicated no pathological behavior.










































## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.


## Warning: Found 322 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
##
## Computed from 12000 by 3736 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -32913.6 1735.6
## p_loo 4426.8 306.9
## looic 65827.1 3471.2
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 2.1]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 3414 91.4% 142
## (0.7, 1] (bad) 156 4.2% <NA>
## (1, Inf) (very bad) 166 4.4% <NA>
## See help('pareto-k-diagnostic') for details.
DHARMa.check_brms.all(pa_sub, integer = TRUE, outliers_type = 'bootstrap')
## DHARMa:testOutliers with type = binomial may have inflated Type I error rates for integer-valued distributions. To get a more exact result, it is recommended to re-run testOutliers with type = 'bootstrap'. See ?testOutliers for details




##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 303, observations = 3736, p-value <
## 2.2e-16
## alternative hypothesis: two.sided
## percent confidence interval:
## 0.000267666 0.002408994
## sample estimates:
## outlier frequency (expected: 0.00133029978586724 )
## 0.08110278
#shinystan::launch_shinystan(pa_sub)
In this instance, the warning about max treedepth is a false
positive. We have set treedepth to 15, and when we check with shinystan,
we see that treedepth is continuously between 10 and 14.
summarize_brms(
pa_sub,
model_rows_fixed = model_rows_fixed_hu,
model_rows_random = model_rows_random_hu,
model_rownames_fixed = model_rownames_fixed_hu,
model_rownames_random = model_rownames_random_hu,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack_hu
)
## Warning in summarize_brms(pa_sub, model_rows_fixed = model_rows_fixed_hu, :
## Coefficients were exponentiated. Double check if this was intended.
|
|
exp(Est.)
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
p_direction
|
|
Intercept
|
62.22*
|
55.52
|
69.76
|
1.003
|
1171.54
|
2180.69
|
1
|
|
Hurdle Intercept
|
1.20
|
0.87
|
1.66
|
1.005
|
2251.72
|
4451.44
|
0.863
|
|
Conditional Within-Person Effects
|
|
Daily persuasion experienced
|
0.98
|
0.90
|
1.06
|
1.003
|
820.00
|
1778.57
|
0.713
|
|
Daily persuasion utilized (partner’s view)
|
1.03
|
0.97
|
1.10
|
1.003
|
1094.97
|
2445.86
|
0.837
|
|
Daily pressure experienced
|
4.46
|
0.63
|
28.97
|
1.001
|
2038.12
|
3516.07
|
0.936
|
|
Daily pressure utilized (partner’s view)
|
0.88
|
0.72
|
1.07
|
1.001
|
2327.95
|
4209.00
|
0.903
|
|
Daily pushing experienced
|
1.05
|
0.89
|
1.27
|
1.002
|
1914.58
|
3765.31
|
0.726
|
|
Daily pushing utilized (partner’s view)
|
0.94
|
0.85
|
1.05
|
1.001
|
2511.46
|
4270.50
|
0.849
|
|
Day
|
0.92*
|
0.90
|
0.95
|
1.000
|
18563.45
|
9338.10
|
1
|
|
Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Conditional Between-Person Effects
|
|
Mean persuasion experienced
|
1.25
|
0.94
|
1.69
|
1.001
|
1050.07
|
2075.26
|
0.936
|
|
Mean persuasion utilized (partner’s view)
|
1.01
|
0.75
|
1.37
|
1.001
|
1044.55
|
2129.30
|
0.518
|
|
Mean pressure experienced
|
1.04
|
0.78
|
1.39
|
1.001
|
1229.87
|
2676.07
|
0.609
|
|
Mean pressure utilized (partner’s view)
|
0.75*
|
0.56
|
1.00
|
1.001
|
1246.71
|
2682.99
|
0.976
|
|
Mean pushing experienced
|
1.14
|
0.76
|
1.71
|
1.003
|
1234.69
|
2416.32
|
0.744
|
|
Mean pushing utilized (partner’s view)
|
1.21
|
0.80
|
1.82
|
1.003
|
1213.71
|
2345.74
|
0.821
|
|
Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hurdle Within-Person Effects
|
|
Hu Daily persuasion experienced
|
0.65*
|
0.57
|
0.73
|
1.001
|
8995.78
|
7874.13
|
1
|
|
Hu Daily persuasion utilized (partner’s view)
|
0.75*
|
0.67
|
0.84
|
1.001
|
8190.82
|
7930.91
|
1
|
|
Hu Daily pressure experienced
|
1.23
|
0.88
|
1.72
|
1.001
|
9605.40
|
8374.53
|
0.897
|
|
Hu Daily pressure utilized (partner’s view)
|
0.66*
|
0.42
|
0.95
|
1.000
|
8978.67
|
6552.79
|
0.989
|
|
Hu Daily pushing experienced
|
0.58*
|
0.40
|
0.78
|
1.000
|
7451.81
|
7612.31
|
1
|
|
Hu Daily pushing utilized (partner’s view)
|
0.54*
|
0.41
|
0.69
|
1.000
|
7785.52
|
7787.54
|
1
|
|
Hu Day
|
1.09
|
0.84
|
1.42
|
1.000
|
18550.37
|
9136.53
|
0.742
|
|
Hu Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hurdle Between-Person Effects
|
|
Hu Mean persuasion experienced
|
0.83
|
0.37
|
1.82
|
1.003
|
2244.54
|
3914.95
|
0.682
|
|
Hu Mean persuasion utilized (partner’s view)
|
0.84
|
0.39
|
1.86
|
1.003
|
2220.38
|
3867.94
|
0.674
|
|
Hu Mean pressure experienced
|
3.32*
|
1.40
|
8.09
|
1.002
|
3571.38
|
5985.08
|
0.996
|
|
Hu Mean pressure utilized (partner’s view)
|
1.79
|
0.75
|
4.23
|
1.003
|
3589.16
|
6384.14
|
0.909
|
|
Hu Mean pushing experienced
|
0.36
|
0.11
|
1.12
|
1.000
|
3642.10
|
5636.41
|
0.964
|
|
Hu Mean pushing utilized (partner’s view)
|
0.34
|
0.11
|
1.06
|
1.000
|
3754.49
|
5708.99
|
0.967
|
|
Hu Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Random Effects
|
|
sd(Intercept)
|
0.35
|
0.27
|
0.44
|
1.00
|
2343.80
|
3699.48
|
NA
|
|
sd(Hurdle Intercept)
|
0.90
|
0.69
|
1.17
|
1.00
|
3156.04
|
5527.48
|
NA
|
|
sd(Daily persuasion experienced)
|
0.25
|
0.20
|
0.32
|
1.00
|
2676.95
|
4342.53
|
NA
|
|
sd(Daily persuasion utilized (partner’s view))
|
0.19
|
0.15
|
0.25
|
1.00
|
2810.89
|
5109.44
|
NA
|
|
sd(Daily pressure experienced)
|
6.41
|
5.07
|
8.07
|
1.00
|
3848.63
|
6466.47
|
NA
|
|
sd(Daily pressure utilized (partner’s view))
|
0.49
|
0.34
|
0.71
|
1.00
|
3944.61
|
6425.50
|
NA
|
|
sd(Daily pushing experienced)
|
0.51
|
0.33
|
0.76
|
1.00
|
2362.12
|
5293.27
|
NA
|
|
sd(Daily pushing utilized (partner’s view))
|
0.31
|
0.23
|
0.40
|
1.00
|
4245.87
|
6674.84
|
NA
|
|
sd(Hu Daily persuasion experienced)
|
0.18
|
0.02
|
0.34
|
1.00
|
3802.82
|
2955.97
|
NA
|
|
sd(Hu Daily persuasion utilized (partner’s view))
|
0.17
|
0.02
|
0.33
|
1.00
|
3017.74
|
2932.29
|
NA
|
|
sd(Hu Daily pressure experienced)
|
0.30
|
0.01
|
0.86
|
1.00
|
4765.88
|
5838.00
|
NA
|
|
sd(Hu Daily pressure utilized (partner’s view))
|
0.35
|
0.01
|
1.02
|
1.00
|
4222.93
|
5749.34
|
NA
|
|
sd(Hu Daily pushing experienced)
|
0.65
|
0.33
|
1.09
|
1.00
|
5623.85
|
7121.92
|
NA
|
|
sd(Hu Daily pushing utilized (partner’s view))
|
0.32
|
0.04
|
0.65
|
1.00
|
4315.72
|
2830.48
|
NA
|
|
Additional Parameters
|
|
ar[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
ma[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
cosy
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
mcmc_plot(pa_sub,
variable = c(
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
),
#regex = TRUE,
type = 'areas',
prob = 0.95)

mcmc_plot(pa_sub,
variable = c(
'b_hu_persuasion_self_cw',
'b_hu_persuasion_partner_cw',
'b_hu_pressure_self_cw',
'b_hu_pressure_partner_cw',
'b_hu_pushing_self_cw',
'b_hu_pushing_partner_cw'
),
#regex = TRUE,
type = 'areas',
prob = 0.95)

bayesfac <- bayestestR::bayesfactor(
pa_sub,
effects = "fixed"
)
## Sampling priors, please wait...
## Warning: Bayes factors might not be precise.
## For precise Bayes factors, sampling at least 40,000 posterior samples is
## recommended.
| 16 |
b_Intercept |
189.7873193 |
fixed |
conditional |
| 3 |
b_hu_Intercept |
-4.4904516 |
fixed |
conditional |
| 20 |
b_persuasion_self_cw |
-3.3856527 |
fixed |
conditional |
| 18 |
b_persuasion_partner_cw |
-3.3383318 |
fixed |
conditional |
| 24 |
b_pressure_self_cw |
0.6984805 |
fixed |
conditional |
| 22 |
b_pressure_partner_cw |
-1.7927693 |
fixed |
conditional |
| 28 |
b_pushing_self_cw |
-2.6773216 |
fixed |
conditional |
| 26 |
b_pushing_partner_cw |
-2.7365966 |
fixed |
conditional |
| 19 |
b_persuasion_self_cb |
-1.1528969 |
fixed |
conditional |
| 17 |
b_persuasion_partner_cb |
-2.3378604 |
fixed |
conditional |
| 23 |
b_pressure_self_cb |
-2.3059791 |
fixed |
conditional |
| 21 |
b_pressure_partner_cb |
-0.2889599 |
fixed |
conditional |
| 27 |
b_pushing_self_cb |
-1.7806387 |
fixed |
conditional |
| 25 |
b_pushing_partner_cb |
-1.5862782 |
fixed |
conditional |
| 1 |
b_day |
9.8115848 |
fixed |
conditional |
| 7 |
b_hu_persuasion_self_cw |
11.0987333 |
fixed |
conditional |
| 5 |
b_hu_persuasion_partner_cw |
6.6446016 |
fixed |
conditional |
| 11 |
b_hu_pressure_self_cw |
-1.8440165 |
fixed |
conditional |
| 9 |
b_hu_pressure_partner_cw |
0.0332200 |
fixed |
conditional |
| 15 |
b_hu_pushing_self_cw |
2.8248046 |
fixed |
conditional |
| 13 |
b_hu_pushing_partner_cw |
7.5653610 |
fixed |
conditional |
| 6 |
b_hu_persuasion_self_cb |
-1.6791465 |
fixed |
conditional |
| 4 |
b_hu_persuasion_partner_cb |
-1.6648689 |
fixed |
conditional |
| 10 |
b_hu_pressure_self_cb |
1.9650910 |
fixed |
conditional |
| 8 |
b_hu_pressure_partner_cb |
-0.8626538 |
fixed |
conditional |
| 14 |
b_hu_pushing_self_cb |
0.1645216 |
fixed |
conditional |
| 12 |
b_hu_pushing_partner_cb |
0.3081348 |
fixed |
conditional |
| 2 |
b_hu_day |
-2.7350012 |
fixed |
conditional |
Additionally, as a sensitivity analysis, we estimate the two part of
the models separately.
Two separate models
Modelling 0/1
The zero vs. 1 modelling part also has high pareto-k values, but
reaches the same conslucsions as the hurdle model. We tried further
simplifying by removing the residual AR1 correlation structure, which
led to a model with good pareto-k values, still arriving at the same
conslusion as the original hurdle model:
df_double$pa_sub_zero <- as.factor(ifelse(df_double$pa_sub > 0, 1, 0))
formula <- bf(
pa_sub_zero ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(1 + persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
set_prior("normal(0, 1.5)", class = "b")
, brms::set_prior("normal(0, 5)", class = "Intercept") # for non-zero PA
, brms::set_prior("normal(0, 1)", class = "sd", group = "coupleID")
#, brms::set_prior("cauchy(0, 2)", class = "sigma", lb = 0)
#, brms::set_prior("cauchy(0, 1.5)", class = "sderr", lb = 0)
#, autocor_prior
)
brms::validate_prior(
prior1,
formula = formula,
data = df_double,
family = bernoulli()
)
## Warning: Rows containing NAs were excluded from the model.
pa_sub_zero_model <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
#family = brms::hurdle_lognormal(),
#family = brms::hurdle_negbinomial(),
family = brms::bernoulli(),
#control = list(adapt_delta = 0.95),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("pa_sub_hu_zero_part_NOAR", suffix))
)
## Warning: Rows containing NAs were excluded from the model.
check_brms(pa_sub_zero_model, log_pp_check = FALSE)
##
## Divergences:
## 0 of 12000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 12000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.





















## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 2 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
##
## Computed from 12000 by 3736 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -2149.7 27.8
## p_loo 93.4 4.1
## looic 4299.5 55.6
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 2.3]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 3734 99.9% 355
## (0.7, 1] (bad) 2 0.1% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
DHARMa.check_brms.all(pa_sub_zero_model, integer = TRUE, outliers_type = 'bootstrap')




##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 0, observations = 3736, p-value = 1
## alternative hypothesis: two.sided
## percent confidence interval:
## 0 0
## sample estimates:
## outlier frequency (expected: 0 )
## 0
summarize_brms(
pa_sub_zero_model,
model_rows_fixed = model_rows_fixed_hu,
model_rows_random = model_rows_random_hu,
model_rownames_fixed = model_rownames_fixed_hu,
model_rownames_random = model_rownames_random_hu,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack_hu
)
|
|
OR
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
p_direction
|
|
Intercept
|
0.84
|
0.61
|
1.15
|
1.001
|
1841.97
|
3973.90
|
0.863
|
|
Hurdle Intercept
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Conditional Within-Person Effects
|
|
Daily persuasion experienced
|
1.53*
|
1.36
|
1.75
|
1.001
|
8062.00
|
8582.55
|
1
|
|
Daily persuasion utilized (partner’s view)
|
1.33*
|
1.19
|
1.50
|
1.000
|
9007.75
|
8833.49
|
1
|
|
Daily pressure experienced
|
0.82
|
0.59
|
1.11
|
1.000
|
10666.20
|
8323.82
|
0.906
|
|
Daily pressure utilized (partner’s view)
|
1.48*
|
1.05
|
2.25
|
1.000
|
10016.51
|
6740.30
|
0.987
|
|
Daily pushing experienced
|
1.72*
|
1.28
|
2.43
|
1.000
|
7256.09
|
7373.66
|
1
|
|
Daily pushing utilized (partner’s view)
|
1.83*
|
1.46
|
2.40
|
1.001
|
8929.39
|
8028.12
|
1
|
|
Day
|
0.92
|
0.71
|
1.19
|
1.000
|
18939.10
|
8954.86
|
0.739
|
|
Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Conditional Between-Person Effects
|
|
Mean persuasion experienced
|
1.17
|
0.56
|
2.48
|
1.002
|
1891.71
|
4153.21
|
0.667
|
|
Mean persuasion utilized (partner’s view)
|
1.17
|
0.57
|
2.44
|
1.002
|
1970.51
|
4133.98
|
0.666
|
|
Mean pressure experienced
|
0.33*
|
0.15
|
0.75
|
1.001
|
2989.43
|
5922.42
|
0.996
|
|
Mean pressure utilized (partner’s view)
|
0.59
|
0.26
|
1.34
|
1.002
|
3409.72
|
5951.96
|
0.9
|
|
Mean pushing experienced
|
2.45
|
0.84
|
7.10
|
1.001
|
3185.19
|
5355.11
|
0.953
|
|
Mean pushing utilized (partner’s view)
|
2.56
|
0.88
|
7.30
|
1.001
|
3160.95
|
5396.08
|
0.958
|
|
Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hurdle Within-Person Effects
|
|
Hu Daily persuasion experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily persuasion utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pressure experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pressure utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pushing experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pushing utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Day
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hurdle Between-Person Effects
|
|
Hu Mean persuasion experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean persuasion utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pressure experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pressure utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pushing experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pushing utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Random Effects
|
|
sd(Intercept)
|
0.89
|
0.68
|
1.14
|
1.00
|
3199.26
|
5243.78
|
NA
|
|
sd(Hurdle Intercept)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Daily persuasion experienced)
|
0.18
|
0.02
|
0.34
|
1.00
|
3164.76
|
2298.66
|
NA
|
|
sd(Daily persuasion utilized (partner’s view))
|
0.17
|
0.02
|
0.33
|
1.00
|
3506.22
|
3289.23
|
NA
|
|
sd(Daily pressure experienced)
|
0.28
|
0.01
|
0.78
|
1.00
|
4188.59
|
5081.55
|
NA
|
|
sd(Daily pressure utilized (partner’s view))
|
0.31
|
0.01
|
0.90
|
1.00
|
3962.03
|
5276.36
|
NA
|
|
sd(Daily pushing experienced)
|
0.62
|
0.32
|
1.01
|
1.00
|
5746.41
|
6689.15
|
NA
|
|
sd(Daily pushing utilized (partner’s view))
|
0.31
|
0.05
|
0.63
|
1.00
|
4373.04
|
3371.61
|
NA
|
|
sd(Hu Daily persuasion experienced)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily persuasion utilized (partner’s view))
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pressure experienced)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pressure utilized (partner’s view))
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pushing experienced)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pushing utilized (partner’s view))
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Additional Parameters
|
|
ar[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
ma[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
cosy
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
mcmc_plot(pa_sub_zero_model,
variable = c(
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
),
#regex = TRUE,
type = 'areas',
prob = 0.95)

Modelling active Days
# Only include active days
df_double$pa_sub_non_zero <- ifelse(df_double$pa_sub > 0, df_double$pa_sub, NA)
formula <- bf(
log(pa_sub_non_zero) ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(1 + persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
##, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 50)", class = "Intercept") # for non-zero PA
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
, brms::set_prior("cauchy(0, 2)", class = "sigma", lb = 0)
#, brms::set_prior("cauchy(0, 2)", class = "sderr", lb = 0)
#, autocor_prior
)
brms::validate_prior(
prior1,
formula = formula,
data = df_double,
family = gaussian()
)
## Warning: Rows containing NAs were excluded from the model.
pa_sub_active_model <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.95),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("pa_sub_hu_active_part_NOAR", suffix))
)
## Warning: Rows containing NAs were excluded from the model.
check_brms(pa_sub_active_model, log_pp_check = TRUE)
##
## Divergences:
## 0 of 12000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 12000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.






















## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.


##
## Computed from 12000 by 1672 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -1785.5 35.3
## p_loo 85.9 4.1
## looic 3571.1 70.6
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 2.0]).
##
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
DHARMa.check_brms.all(pa_sub_active_model, integer = TRUE, outliers_type = 'bootstrap')
## DHARMa:testOutliers with type = binomial may have inflated Type I error rates for integer-valued distributions. To get a more exact result, it is recommended to re-run testOutliers with type = 'bootstrap'. See ?testOutliers for details




##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 8, observations = 1672, p-value = 0.26
## alternative hypothesis: two.sided
## percent confidence interval:
## 0.0005980861 0.0068929426
## sample estimates:
## outlier frequency (expected: 0.00320574162679426 )
## 0.004784689
summarize_brms(
pa_sub_active_model,
model_rows_fixed = model_rows_fixed_hu,
model_rows_random = model_rows_random_hu,
model_rownames_fixed = model_rownames_fixed_hu,
model_rownames_random = model_rownames_random_hu,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack_hu
)
## Warning in summarize_brms(pa_sub_active_model, model_rows_fixed =
## model_rows_fixed_hu, : Coefficients were exponentiated. Double check if this
## was intended.
|
|
exp(Est.)
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
p_direction
|
|
Intercept
|
47.85*
|
42.22
|
54.17
|
1.001
|
4041.60
|
6381.47
|
1
|
|
Hurdle Intercept
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Conditional Within-Person Effects
|
|
Daily persuasion experienced
|
1.03
|
0.97
|
1.08
|
1.000
|
6597.52
|
8346.37
|
0.833
|
|
Daily persuasion utilized (partner’s view)
|
1.03
|
0.99
|
1.08
|
1.000
|
8955.61
|
9602.22
|
0.904
|
|
Daily pressure experienced
|
0.89*
|
0.80
|
0.99
|
1.000
|
14496.32
|
8806.27
|
0.987
|
|
Daily pressure utilized (partner’s view)
|
0.94
|
0.85
|
1.03
|
1.000
|
13817.24
|
8187.83
|
0.908
|
|
Daily pushing experienced
|
1.03
|
0.96
|
1.10
|
1.000
|
11709.14
|
9409.07
|
0.775
|
|
Daily pushing utilized (partner’s view)
|
0.99
|
0.93
|
1.05
|
1.000
|
11995.74
|
10105.19
|
0.633
|
|
Day
|
1.01
|
0.89
|
1.14
|
1.000
|
20035.93
|
9281.40
|
0.545
|
|
Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Conditional Between-Person Effects
|
|
Mean persuasion experienced
|
1.00
|
0.73
|
1.37
|
1.001
|
3231.81
|
4989.35
|
0.51
|
|
Mean persuasion utilized (partner’s view)
|
0.98
|
0.72
|
1.33
|
1.001
|
3286.72
|
5489.83
|
0.56
|
|
Mean pressure experienced
|
1.15
|
0.80
|
1.64
|
1.000
|
5002.27
|
7220.72
|
0.778
|
|
Mean pressure utilized (partner’s view)
|
0.89
|
0.61
|
1.28
|
1.000
|
5018.40
|
7058.04
|
0.732
|
|
Mean pushing experienced
|
1.34
|
0.84
|
2.11
|
1.000
|
4624.61
|
7355.15
|
0.896
|
|
Mean pushing utilized (partner’s view)
|
1.42
|
0.89
|
2.26
|
1.000
|
4706.03
|
7431.81
|
0.929
|
|
Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hurdle Within-Person Effects
|
|
Hu Daily persuasion experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily persuasion utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pressure experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pressure utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pushing experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pushing utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Day
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hurdle Between-Person Effects
|
|
Hu Mean persuasion experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean persuasion utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pressure experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pressure utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pushing experienced
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pushing utilized (partner’s view)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Random Effects
|
|
sd(Intercept)
|
0.32
|
0.25
|
0.42
|
1.00
|
3621.92
|
6455.57
|
NA
|
|
sd(Hurdle Intercept)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Daily persuasion experienced)
|
0.12
|
0.08
|
0.17
|
1.00
|
7194.08
|
9303.03
|
NA
|
|
sd(Daily persuasion utilized (partner’s view))
|
0.09
|
0.05
|
0.13
|
1.00
|
8556.16
|
8646.99
|
NA
|
|
sd(Daily pressure experienced)
|
0.08
|
0.00
|
0.23
|
1.00
|
5748.83
|
6157.17
|
NA
|
|
sd(Daily pressure utilized (partner’s view))
|
0.07
|
0.00
|
0.19
|
1.00
|
6534.51
|
6674.27
|
NA
|
|
sd(Daily pushing experienced)
|
0.11
|
0.04
|
0.19
|
1.00
|
5752.73
|
4564.11
|
NA
|
|
sd(Daily pushing utilized (partner’s view))
|
0.09
|
0.02
|
0.17
|
1.00
|
5084.36
|
3390.77
|
NA
|
|
sd(Hu Daily persuasion experienced)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily persuasion utilized (partner’s view))
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pressure experienced)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pressure utilized (partner’s view))
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pushing experienced)
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pushing utilized (partner’s view))
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Additional Parameters
|
|
ar[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
ma[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
cosy
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
0.68
|
0.66
|
0.71
|
1.00
|
19220.03
|
8549.91
|
NA
|
mcmc_plot(pa_sub_active_model,
variable = c(
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
),
#regex = TRUE,
type = 'areas',
prob = 0.95)

Device Based MVPA
range(df_double$pa_obj, na.rm = T)
## [1] 5.75 971.25
hist(df_double$pa_obj, breaks = 50)

df_double$pa_obj_log <- log(df_double$pa_obj)
hist(df_double$pa_obj_log, breaks = 50)

log gaussian
formula <- bf(
log(pa_obj) ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day + weartime_self_cw + weartime_self_cb +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 50)", class = "Intercept") # for non-zero PA
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
, brms::set_prior("cauchy(0, 2)", class = "sigma", lb = 0)
#, brms::set_prior("cauchy(0, 2)", class = "sderr", lb = 0)
#, autocor_prior
)
brms::validate_prior(
prior1,
formula = formula,
data = df_double,
family = gaussian()
)
## Warning: Rows containing NAs were excluded from the model.
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
pa_obj_log <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.95),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("pa_obj_log_gaussian_NOAR", suffix))
)
## Warning: Rows containing NAs were excluded from the model.
check_brms(pa_obj_log, log_pp_check = TRUE)
##
## Divergences:
## 0 of 12000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 12000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.























## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.


##
## Computed from 12000 by 3337 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -2936.5 56.3
## p_loo 92.1 4.5
## looic 5872.9 112.6
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 2.3]).
##
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
DHARMa.check_brms.all(pa_obj_log, integer = TRUE, outliers_type = 'bootstrap')
## DHARMa:testOutliers with type = binomial may have inflated Type I error rates for integer-valued distributions. To get a more exact result, it is recommended to re-run testOutliers with type = 'bootstrap'. See ?testOutliers for details




##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 26, observations = 3337, p-value < 2.2e-16
## alternative hypothesis: two.sided
## percent confidence interval:
## 0.001341025 0.004952053
## sample estimates:
## outlier frequency (expected: 0.00295474977524723 )
## 0.007791429
summarize_brms(
pa_obj_log,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack)
## Warning in summarize_brms(pa_obj_log, model_rows_fixed = model_rows_fixed, :
## Coefficients were exponentiated. Double check if this was intended.
|
|
exp(Est.)
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
p_direction
|
|
Intercept
|
117.41*
|
105.48
|
130.41
|
1.002
|
1936.20
|
4337.49
|
1
|
|
Within-Person Effects
|
|
Daily persuasion experienced
|
1.03
|
1.00
|
1.06
|
1.000
|
8443.32
|
9440.38
|
0.966
|
|
Daily persuasion utilized (partner’s view)
|
1.02
|
0.99
|
1.05
|
1.001
|
10110.26
|
9523.00
|
0.888
|
|
Daily pressure experienced
|
0.94
|
0.88
|
1.01
|
1.000
|
13123.29
|
8882.26
|
0.96
|
|
Daily pressure utilized (partner’s view)
|
0.98
|
0.92
|
1.05
|
1.000
|
14150.05
|
9457.65
|
0.714
|
|
Daily pushing experienced
|
1.03
|
0.98
|
1.08
|
1.000
|
11136.01
|
8673.71
|
0.9
|
|
Daily pushing utilized (partner’s view)
|
1.02
|
0.97
|
1.06
|
1.000
|
15288.78
|
9998.46
|
0.771
|
|
Day
|
0.97
|
0.91
|
1.04
|
1.000
|
20771.69
|
8890.89
|
0.785
|
|
Daily weartime
|
1.00*
|
1.00
|
1.00
|
1.000
|
12258.92
|
8147.17
|
1
|
|
Between-Person Effects
|
|
Mean persuasion experienced
|
1.10
|
0.82
|
1.46
|
1.003
|
1635.21
|
3066.39
|
0.748
|
|
Mean persuasion utilized (partner’s view)
|
0.98
|
0.73
|
1.30
|
1.003
|
1664.34
|
3415.83
|
0.559
|
|
Mean pressure experienced
|
0.98
|
0.73
|
1.31
|
1.003
|
2233.86
|
4590.15
|
0.56
|
|
Mean pressure utilized (partner’s view)
|
0.97
|
0.73
|
1.28
|
1.002
|
2141.33
|
4170.53
|
0.594
|
|
Mean pushing experienced
|
0.97
|
0.65
|
1.45
|
1.002
|
2615.53
|
4583.92
|
0.553
|
|
Mean pushing utilized (partner’s view)
|
1.25
|
0.83
|
1.85
|
1.002
|
2546.44
|
4664.09
|
0.859
|
|
Mean weartime
|
1.00
|
1.00
|
1.00
|
1.000
|
16930.39
|
10509.93
|
0.909
|
|
Random Effects
|
|
sd(Intercept)
|
0.31
|
0.24
|
0.40
|
1.00
|
2373.00
|
4540.45
|
NA
|
|
sd(Daily persuasion experienced)
|
0.05
|
0.02
|
0.08
|
1.00
|
6108.14
|
4438.88
|
NA
|
|
sd(Daily persuasion utilized (partner’s view))
|
0.06
|
0.03
|
0.09
|
1.00
|
6116.29
|
5901.98
|
NA
|
|
sd(Daily pressure experienced)
|
0.05
|
0.00
|
0.14
|
1.00
|
5104.76
|
5590.89
|
NA
|
|
sd(Daily pressure utilized (partner’s view))
|
0.04
|
0.00
|
0.12
|
1.00
|
6898.74
|
5731.73
|
NA
|
|
sd(Daily pushing experienced)
|
0.07
|
0.01
|
0.15
|
1.00
|
2745.29
|
3354.31
|
NA
|
|
sd(Daily pushing utilized (partner’s view))
|
0.04
|
0.00
|
0.10
|
1.00
|
4176.37
|
5266.68
|
NA
|
|
Additional Parameters
|
|
ar[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
ma[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
cosy
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
0.57
|
0.56
|
0.59
|
1.00
|
19512.21
|
8255.47
|
NA
|
mcmc_plot(pa_obj_log,
variable = c(
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
),
#regex = TRUE,
type = 'areas',
prob = 0.95)

Affect
range(df_double$aff, na.rm = T)
## [1] 0 5
hist(df_double$aff, breaks = 15)

Gaussian
formula <- bf(
aff ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 5)", class = "b")
,brms::set_prior("normal(0, 20)", class = "Intercept", lb=1, ub=6) # range of the outcome scale
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
, brms::set_prior("cauchy(0, 2)", class = "sigma", lb = 0)
#, brms::set_prior("cauchy(0, 2)", class = "sderr", lb = 0)
#, autocor_prior
)
brms::validate_prior(
prior1,
formula = formula,
data = df_double,
family = gaussian()
)
## Warning: Rows containing NAs were excluded from the model.
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
mood_gauss <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("mood_gauss_NOAR", suffix))
)
## Warning: Rows containing NAs were excluded from the model.
check_brms(mood_gauss, log_pp_check = FALSE)
##
## Divergences:
## 0 of 12000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 12000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.






















## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

##
## Computed from 12000 by 3736 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -5185.9 59.2
## p_loo 75.2 3.3
## looic 10371.7 118.5
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 2.1]).
##
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
DHARMa.check_brms.all(mood_gauss, integer = FALSE)




##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: model.check
## outliers at both margin(s) = 31, observations = 3736, p-value =
## 9.752e-11
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
## 0.00564461 0.01175733
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 )
## 0.008297645
summarize_brms(
mood_gauss,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = F) %>%
print_df(rows_to_pack = rows_to_pack)
|
|
b
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
p_direction
|
|
Intercept
|
3.70*
|
3.48
|
3.91
|
1.007
|
1299.69
|
2626.71
|
1
|
|
Within-Person Effects
|
|
Daily persuasion experienced
|
0.00
|
-0.04
|
0.05
|
1.000
|
10096.58
|
8360.31
|
0.553
|
|
Daily persuasion utilized (partner’s view)
|
0.02
|
-0.02
|
0.07
|
1.001
|
8776.31
|
8901.48
|
0.829
|
|
Daily pressure experienced
|
-0.04
|
-0.14
|
0.07
|
1.000
|
11418.22
|
8197.57
|
0.767
|
|
Daily pressure utilized (partner’s view)
|
-0.02
|
-0.14
|
0.08
|
1.000
|
10703.53
|
8437.61
|
0.661
|
|
Daily pushing experienced
|
0.02
|
-0.04
|
0.09
|
1.000
|
11578.47
|
8985.61
|
0.768
|
|
Daily pushing utilized (partner’s view)
|
0.08*
|
0.01
|
0.14
|
1.000
|
10076.04
|
8287.53
|
0.984
|
|
Day
|
0.26*
|
0.15
|
0.37
|
1.001
|
17305.49
|
8858.10
|
1
|
|
Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Between-Person Effects
|
|
Mean persuasion experienced
|
0.33
|
-0.21
|
0.90
|
1.003
|
1324.40
|
2167.41
|
0.889
|
|
Mean persuasion utilized (partner’s view)
|
0.22
|
-0.32
|
0.79
|
1.003
|
1322.88
|
2252.89
|
0.79
|
|
Mean pressure experienced
|
-0.31
|
-0.86
|
0.23
|
1.003
|
1481.77
|
2769.72
|
0.874
|
|
Mean pressure utilized (partner’s view)
|
-0.30
|
-0.84
|
0.24
|
1.003
|
1476.58
|
2758.08
|
0.871
|
|
Mean pushing experienced
|
0.22
|
-0.54
|
0.99
|
1.002
|
1997.17
|
3840.31
|
0.714
|
|
Mean pushing utilized (partner’s view)
|
0.35
|
-0.39
|
1.12
|
1.002
|
2005.43
|
3822.38
|
0.825
|
|
Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Random Effects
|
|
sd(Intercept)
|
0.60
|
0.47
|
0.78
|
1.00
|
2148.52
|
4074.04
|
NA
|
|
sd(Daily persuasion experienced)
|
0.04
|
0.00
|
0.10
|
1.00
|
3348.96
|
5109.75
|
NA
|
|
sd(Daily persuasion utilized (partner’s view))
|
0.08
|
0.01
|
0.13
|
1.00
|
3084.49
|
2127.56
|
NA
|
|
sd(Daily pressure experienced)
|
0.08
|
0.00
|
0.24
|
1.00
|
4964.63
|
5526.49
|
NA
|
|
sd(Daily pressure utilized (partner’s view))
|
0.09
|
0.00
|
0.26
|
1.00
|
4911.35
|
5368.11
|
NA
|
|
sd(Daily pushing experienced)
|
0.06
|
0.00
|
0.14
|
1.00
|
3656.74
|
3408.48
|
NA
|
|
sd(Daily pushing utilized (partner’s view))
|
0.07
|
0.00
|
0.17
|
1.00
|
4184.55
|
4073.31
|
NA
|
|
Additional Parameters
|
|
ar[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
ma[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
cosy
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
0.96
|
0.94
|
0.98
|
1.00
|
18080.51
|
9327.96
|
NA
|
mcmc_plot(mood_gauss,
variable = c(
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
),
#regex = TRUE,
type = 'areas',
prob = 0.95)

Reactance
range(df_double$reactance, na.rm = T)
## [1] 0 5
hist(df_double$reactance, breaks = 7)

hist(log(df_double$reactance+0.1), breaks = 10)

Ordinal
df_double$reactance_ordinal <- factor(df_double$reactance,
levels = 0:5,
ordered = TRUE)
formula <- bf(
reactance_ordinal ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
#, brms::set_prior("normal(0, 10)", class = "Intercept", lb=0, ub=5) # range of the outcome scale
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
#, brms::set_prior("cauchy(0, 2)", class = "sigma", lb = 0)
#, brms::set_prior("cauchy(0, 2)", class = "sderr", lb = 0)
#, autocor_prior
)
brms::validate_prior(
prior1,
formula = formula,
data = df_double,
family = cumulative() # HURDLE_CUMULATIVE
)
## Warning: Rows containing NAs were excluded from the model.
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
reactance_ordinal <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::cumulative(),
#control = list(adapt_delta = 0.95),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777
, file = file.path("models_cache_brms", paste0("reactance_ordinal_NOARNOAR_", suffix))
)
## Warning: Rows containing NAs were excluded from the model.
check_brms(reactance_ordinal)
##
## Divergences:
## 0 of 12000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 12000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
























## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 6 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
##
## Computed from 12000 by 756 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -681.9 31.9
## p_loo 73.4 5.4
## looic 1363.8 63.8
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 1.7]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 750 99.2% 445
## (0.7, 1] (bad) 6 0.8% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
DHARMa.check_brms.all(reactance_ordinal, outliers_type = 'bootstrap')




##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 2, observations = 756, p-value = 0.08
## alternative hypothesis: two.sided
## percent confidence interval:
## 0.000000000 0.002645503
## sample estimates:
## outlier frequency (expected: 0.00041005291005291 )
## 0.002645503
summarize_brms(
reactance_ordinal,
model_rows_fixed = model_rows_fixed_ordinal,
model_rows_random = model_rows_random_ordinal,
model_rownames_fixed = model_rownames_fixed_ordinal,
model_rownames_random = model_rownames_random_ordinal,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack_ordinal)
|
|
OR
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
p_direction
|
|
Intercepts
|
|
Intercept
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Intercept[1]
|
3.85*
|
2.33
|
6.45
|
1.000
|
8597.51
|
9211.52
|
1
|
|
Intercept[2]
|
8.35*
|
4.95
|
14.45
|
1.000
|
8834.13
|
8761.87
|
1
|
|
Intercept[3]
|
23.24*
|
13.13
|
42.31
|
1.000
|
9373.11
|
9135.25
|
1
|
|
Intercept[4]
|
101.58*
|
52.10
|
209.10
|
1.000
|
10595.59
|
9691.96
|
1
|
|
Intercept[5]
|
3488.39*
|
1077.21
|
13336.21
|
1.001
|
13289.90
|
8614.35
|
1
|
|
Within-Person Effects
|
|
Daily persuasion experienced
|
0.85*
|
0.71
|
0.99
|
1.000
|
9896.79
|
7775.98
|
0.98
|
|
Daily persuasion utilized (partner’s view)
|
1.03
|
0.84
|
1.24
|
1.000
|
9229.64
|
7954.31
|
0.607
|
|
Daily pressure experienced
|
1.84*
|
1.18
|
2.68
|
1.001
|
5608.70
|
6574.26
|
0.994
|
|
Daily pressure utilized (partner’s view)
|
1.22
|
0.71
|
2.06
|
1.001
|
7420.47
|
6712.38
|
0.808
|
|
Daily pushing experienced
|
1.17
|
0.97
|
1.43
|
1.001
|
7757.54
|
7485.59
|
0.946
|
|
Daily pushing utilized (partner’s view)
|
0.91
|
0.71
|
1.17
|
1.000
|
9792.98
|
8248.45
|
0.77
|
|
Day
|
1.47
|
0.75
|
2.89
|
1.000
|
13856.59
|
9556.08
|
0.871
|
|
Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Between-Person Effects
|
|
Mean persuasion experienced
|
1.12
|
0.40
|
3.11
|
1.000
|
4004.78
|
6205.61
|
0.586
|
|
Mean persuasion utilized (partner’s view)
|
1.38
|
0.45
|
4.31
|
1.000
|
4278.63
|
6604.64
|
0.711
|
|
Mean pressure experienced
|
3.51*
|
1.18
|
10.71
|
1.000
|
4792.44
|
6629.78
|
0.99
|
|
Mean pressure utilized (partner’s view)
|
1.17
|
0.37
|
3.66
|
1.000
|
4762.77
|
6973.89
|
0.612
|
|
Mean pushing experienced
|
1.23
|
0.28
|
5.62
|
1.000
|
5266.91
|
7722.24
|
0.605
|
|
Mean pushing utilized (partner’s view)
|
0.11*
|
0.02
|
0.64
|
1.000
|
7169.66
|
7894.75
|
0.992
|
|
Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Random Effects
|
|
sd(Intercept)
|
0.82
|
0.47
|
1.25
|
1.00
|
4256.66
|
7026.55
|
NA
|
|
sd(Daily persuasion experienced)
|
0.18
|
0.01
|
0.43
|
1.00
|
1818.16
|
4469.30
|
NA
|
|
sd(Daily persuasion utilized (partner’s view))
|
0.22
|
0.01
|
0.51
|
1.00
|
3273.96
|
4646.21
|
NA
|
|
sd(Daily pressure experienced)
|
0.56
|
0.09
|
1.14
|
1.00
|
2689.26
|
2386.75
|
NA
|
|
sd(Daily pressure utilized (partner’s view))
|
0.50
|
0.02
|
1.55
|
1.00
|
2929.20
|
4951.16
|
NA
|
|
sd(Daily pushing experienced)
|
0.22
|
0.01
|
0.50
|
1.00
|
3370.38
|
4130.06
|
NA
|
|
sd(Daily pushing utilized (partner’s view))
|
0.18
|
0.01
|
0.57
|
1.00
|
4830.97
|
5284.06
|
NA
|
|
Additional Parameters
|
|
ar[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
ma[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
cosy
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
disc
|
1.00
|
1.00
|
1.00
|
NA
|
NA
|
NA
|
NA
|
mcmc_plot(reactance_ordinal,
variable = c(
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
),
#regex = TRUE,
type = 'areas',
prob = 0.95)

Binary
introduce_binary_reactance <- function(data) {
data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
return(data)
}
df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
for (i in seq_along(implist)) {
implist[[i]] <- introduce_binary_reactance(implist[[i]])
}
}
formula <- bf(
is_reactance ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 10)", class = "Intercept", lb=0, ub=5) # range of the outcome scale
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
#, brms::set_prior("cauchy(0, 2)", class = "sigma", lb = 0)
#, brms::set_prior("cauchy(0, 2)", class = "sderr", lb = 0)
#, autocor_prior
)
brms::validate_prior(
prior1,
formula = formula,
data = df_double,
family = bernoulli()
)
## Warning: Rows containing NAs were excluded from the model.
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
is_reactance <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::bernoulli(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("is_reactance_NOARNOAR_", suffix))
#, file_refit = 'always'
)
## Warning: Rows containing NAs were excluded from the model.
##
## Divergences:
## 0 of 12000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 12000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.





















## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 55 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
##
## Computed from 12000 by 756 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -363.1 16.0
## p_loo 80.1 6.0
## looic 726.3 32.0
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 1.5]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 701 92.7% 429
## (0.7, 1] (bad) 49 6.5% <NA>
## (1, Inf) (very bad) 6 0.8% <NA>
## See help('pareto-k-diagnostic') for details.
DHARMa.check_brms.all(is_reactance, integer = FALSE)




##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: model.check
## outliers at both margin(s) = 1, observations = 756, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
## 0.0000334886 0.0073476538
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 )
## 0.001322751
summarize_brms(
is_reactance,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack)
|
|
OR
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
p_direction
|
|
Intercept
|
0.29*
|
0.16
|
0.50
|
1.000
|
13412.80
|
10197.69
|
1
|
|
Within-Person Effects
|
|
Daily persuasion experienced
|
0.84
|
0.69
|
1.01
|
1.001
|
12653.33
|
8961.78
|
0.966
|
|
Daily persuasion utilized (partner’s view)
|
1.13
|
0.85
|
1.54
|
1.000
|
9878.26
|
8807.43
|
0.789
|
|
Daily pressure experienced
|
2.04*
|
1.03
|
4.57
|
1.000
|
8289.60
|
6530.31
|
0.979
|
|
Daily pressure utilized (partner’s view)
|
1.44
|
0.58
|
4.04
|
1.000
|
8810.48
|
6987.21
|
0.799
|
|
Daily pushing experienced
|
1.28*
|
1.01
|
1.64
|
1.001
|
12865.46
|
8547.86
|
0.979
|
|
Daily pushing utilized (partner’s view)
|
0.89
|
0.60
|
1.31
|
1.000
|
14162.09
|
8870.67
|
0.735
|
|
Day
|
1.64
|
0.77
|
3.51
|
1.000
|
19648.18
|
9310.83
|
0.9
|
|
Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Between-Person Effects
|
|
Mean persuasion experienced
|
1.99
|
0.60
|
6.80
|
1.000
|
6422.05
|
7842.49
|
0.87
|
|
Mean persuasion utilized (partner’s view)
|
1.90
|
0.52
|
7.15
|
1.000
|
6799.76
|
8735.29
|
0.829
|
|
Mean pressure experienced
|
17.91*
|
2.33
|
159.59
|
1.000
|
8975.03
|
8541.37
|
0.997
|
|
Mean pressure utilized (partner’s view)
|
2.27
|
0.24
|
19.14
|
1.000
|
8119.78
|
8948.63
|
0.769
|
|
Mean pushing experienced
|
0.83
|
0.12
|
6.30
|
1.000
|
8730.85
|
8605.26
|
0.58
|
|
Mean pushing utilized (partner’s view)
|
0.08*
|
0.01
|
0.66
|
1.000
|
10022.27
|
10004.57
|
0.99
|
|
Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Random Effects
|
|
sd(Intercept)
|
1.18
|
0.75
|
1.74
|
1.00
|
5382.54
|
7890.40
|
NA
|
|
sd(Daily persuasion experienced)
|
0.21
|
0.01
|
0.51
|
1.00
|
2939.60
|
5289.74
|
NA
|
|
sd(Daily persuasion utilized (partner’s view))
|
0.50
|
0.12
|
0.97
|
1.00
|
4502.23
|
4854.72
|
NA
|
|
sd(Daily pressure experienced)
|
1.12
|
0.16
|
2.43
|
1.00
|
2982.38
|
3385.24
|
NA
|
|
sd(Daily pressure utilized (partner’s view))
|
0.97
|
0.04
|
2.73
|
1.00
|
4249.53
|
5650.95
|
NA
|
|
sd(Daily pushing experienced)
|
0.25
|
0.01
|
0.60
|
1.00
|
4754.94
|
5282.30
|
NA
|
|
sd(Daily pushing utilized (partner’s view))
|
0.31
|
0.01
|
0.95
|
1.00
|
4810.38
|
6592.98
|
NA
|
|
Additional Parameters
|
|
ar[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
ma[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
cosy
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
mcmc_plot(is_reactance,
variable = c(
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
),
#regex = TRUE,
type = 'areas',
prob = 0.95)

hypothesis(is_reactance, "pressure_self_cw > pushing_self_cw")
## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (pressure_self_cw... > 0 0.47 0.39 -0.15 1.13 8.86
## Post.Prob Star
## 1 0.9
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.
Report All Models
if (report_ordinal) {
model_rows_random_final <- model_rows_random_ordinal
model_rows_fixed_final <- model_rows_fixed_ordinal
model_rownames_fixed_final <- model_rownames_fixed_ordinal
model_rownames_random_final <- model_rownames_random_ordinal
rows_to_pack_final <- rows_to_pack_ordinal
} else {
model_rows_random_final <- model_rows_random_hu
model_rows_fixed_final <- model_rows_fixed_hu
model_rownames_fixed_final <- model_rownames_fixed_hu
model_rownames_random_final <- model_rownames_random_hu
rows_to_pack_final <- rows_to_pack_hu
}
all_models <- report_side_by_side(
pa_sub,
pa_obj_log,
mood_gauss,
reactance_ordinal,
is_reactance,
model_rows_random = model_rows_random_final,
model_rows_fixed = model_rows_fixed_final,
model_rownames_random = model_rownames_random_final,
model_rownames_fixed = model_rownames_fixed_final
)
## [1] "pa_sub"
## Warning in summarize_brms(model, short_version = TRUE, exponentiate =
## exponentiate, : Coefficients were exponentiated. Double check if this was
## intended.
## [1] "pa_obj_log"
## Warning in summarize_brms(model, short_version = TRUE, exponentiate =
## exponentiate, : Coefficients were exponentiated. Double check if this was
## intended.
## [1] "mood_gauss"
## [1] "reactance_ordinal"
## [1] "is_reactance"
# pretty printing
summary_all_models <- all_models %>%
print_df(rows_to_pack = rows_to_pack_final) %>%
add_header_above(
c(" ", "Subjective MVPA Hurdle Poisson" = 2,
"Device-Based MVPA Log (Gaussian)" = 2,
"Mood Gaussian" = 2,
"Reactance Ordinal" = 2,
"Reactance Dichotome" = 2)
)
export_xlsx(
summary_all_models,
rows_to_pack = rows_to_pack_final,
file.path("Output", "AllModels_experimental_noAR.xlsx"),
merge_option = 'both',
simplify_2nd_row = TRUE,
colwidths = c(38, 7.2, 13.3, 7.2, 13.3,7.2, 13.3,7.2, 13.3,7.2, 13.3),
line_above_rows = c(1,2),
line_below_rows = c(-1)
)
##
## Attaching package: 'rvest'
## The following object is masked from 'package:readr':
##
## guess_encoding
|
|
Subjective MVPA Hurdle Poisson
|
Device-Based MVPA Log (Gaussian)
|
Mood Gaussian
|
Reactance Ordinal
|
Reactance Dichotome
|
|
|
exp(Est.) pa_sub
|
p_direction pa_sub
|
exp(Est.) pa_obj_log
|
p_direction pa_obj_log
|
b mood_gauss
|
p_direction mood_gauss
|
OR reactance_ordinal
|
p_direction reactance_ordinal
|
OR is_reactance
|
p_direction is_reactance
|
|
Intercept
|
62.22*
|
1
|
117.41*
|
1
|
3.70*
|
1
|
NA
|
NA
|
0.29*
|
1
|
|
Hurdle Intercept
|
1.20
|
0.863
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Conditional Within-Person Effects
|
|
Daily persuasion experienced
|
0.98
|
0.713
|
1.03
|
0.966
|
0.00
|
0.553
|
0.85*
|
0.98
|
0.84
|
0.966
|
|
Daily persuasion utilized (partner’s view)
|
1.03
|
0.837
|
1.02
|
0.888
|
0.02
|
0.829
|
1.03
|
0.607
|
1.13
|
0.789
|
|
Daily pressure experienced
|
4.46
|
0.936
|
0.94
|
0.96
|
-0.04
|
0.767
|
1.84*
|
0.994
|
2.04*
|
0.979
|
|
Daily pressure utilized (partner’s view)
|
0.88
|
0.903
|
0.98
|
0.714
|
-0.02
|
0.661
|
1.22
|
0.808
|
1.44
|
0.799
|
|
Daily pushing experienced
|
1.05
|
0.726
|
1.03
|
0.9
|
0.02
|
0.768
|
1.17
|
0.946
|
1.28*
|
0.979
|
|
Daily pushing utilized (partner’s view)
|
0.94
|
0.849
|
1.02
|
0.771
|
0.08*
|
0.984
|
0.91
|
0.77
|
0.89
|
0.735
|
|
Day
|
0.92*
|
1
|
0.97
|
0.785
|
0.26*
|
1
|
1.47
|
0.871
|
1.64
|
0.9
|
|
Daily weartime
|
NA
|
NA
|
1.00*
|
1
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Conditional Between-Person Effects
|
|
Mean persuasion experienced
|
1.25
|
0.936
|
1.10
|
0.748
|
0.33
|
0.889
|
1.12
|
0.586
|
1.99
|
0.87
|
|
Mean persuasion utilized (partner’s view)
|
1.01
|
0.518
|
0.98
|
0.559
|
0.22
|
0.79
|
1.38
|
0.711
|
1.90
|
0.829
|
|
Mean pressure experienced
|
1.04
|
0.609
|
0.98
|
0.56
|
-0.31
|
0.874
|
3.51*
|
0.99
|
17.91*
|
0.997
|
|
Mean pressure utilized (partner’s view)
|
0.75*
|
0.976
|
0.97
|
0.594
|
-0.30
|
0.871
|
1.17
|
0.612
|
2.27
|
0.769
|
|
Mean pushing experienced
|
1.14
|
0.744
|
0.97
|
0.553
|
0.22
|
0.714
|
1.23
|
0.605
|
0.83
|
0.58
|
|
Mean pushing utilized (partner’s view)
|
1.21
|
0.821
|
1.25
|
0.859
|
0.35
|
0.825
|
0.11*
|
0.992
|
0.08*
|
0.99
|
|
Mean weartime
|
NA
|
NA
|
1.00
|
0.909
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hurdle Within-Person Effects
|
|
Hu Daily persuasion experienced
|
0.65*
|
1
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily persuasion utilized (partner’s view)
|
0.75*
|
1
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pressure experienced
|
1.23
|
0.897
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pressure utilized (partner’s view)
|
0.66*
|
0.989
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pushing experienced
|
0.58*
|
1
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily pushing utilized (partner’s view)
|
0.54*
|
1
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Day
|
1.09
|
0.742
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hurdle Between-Person Effects
|
|
Hu Mean persuasion experienced
|
0.83
|
0.682
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean persuasion utilized (partner’s view)
|
0.84
|
0.674
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pressure experienced
|
3.32*
|
0.996
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pressure utilized (partner’s view)
|
1.79
|
0.909
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pushing experienced
|
0.36
|
0.964
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean pushing utilized (partner’s view)
|
0.34
|
0.967
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Hu Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Random Effects
|
|
sd(Intercept)
|
0.35
|
NA
|
0.31
|
NA
|
0.60
|
NA
|
0.82
|
NA
|
1.18
|
NA
|
|
sd(Hurdle Intercept)
|
0.90
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Daily persuasion experienced)
|
0.25
|
NA
|
0.05
|
NA
|
0.04
|
NA
|
0.18
|
NA
|
0.21
|
NA
|
|
sd(Daily persuasion utilized (partner’s view))
|
0.19
|
NA
|
0.06
|
NA
|
0.08
|
NA
|
0.22
|
NA
|
0.50
|
NA
|
|
sd(Daily pressure experienced)
|
6.41
|
NA
|
0.05
|
NA
|
0.08
|
NA
|
0.56
|
NA
|
1.12
|
NA
|
|
sd(Daily pressure utilized (partner’s view))
|
0.49
|
NA
|
0.04
|
NA
|
0.09
|
NA
|
0.50
|
NA
|
0.97
|
NA
|
|
sd(Daily pushing experienced)
|
0.51
|
NA
|
0.07
|
NA
|
0.06
|
NA
|
0.22
|
NA
|
0.25
|
NA
|
|
sd(Daily pushing utilized (partner’s view))
|
0.31
|
NA
|
0.04
|
NA
|
0.07
|
NA
|
0.18
|
NA
|
0.31
|
NA
|
|
sd(Hu Daily persuasion experienced)
|
0.18
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily persuasion utilized (partner’s view))
|
0.17
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pressure experienced)
|
0.30
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pressure utilized (partner’s view))
|
0.35
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pushing experienced)
|
0.65
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sd(Hu Daily pushing utilized (partner’s view))
|
0.32
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Additional Parameters
|
|
ar[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
ma[1]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
cosy
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
NA
|
NA
|
0.57
|
NA
|
0.96
|
NA
|
NA
|
NA
|
NA
|
NA
|
Analyses were conducted using the R Statistical language (version
4.4.1; R Core Team, 2024) on Windows 11 x64 (build 22635)
report::report_packages()
- beepr (version 2.0; Bååth R, 2024)
- R.methodsS3 (version 1.8.2; Bengtsson H, 2003)
- R.oo (version 1.26.0; Bengtsson H, 2003)
- R.utils (version 2.12.3; Bengtsson H, 2023)
- brms (version 2.21.0; Bürkner P, 2017)
- Rcpp (version 1.0.13; Eddelbuettel D et al., 2024)
- bayesplot (version 1.11.1; Gabry J, Mahr T, 2024)
- lubridate (version 1.9.3; Grolemund G, Wickham H, 2011)
- DHARMa (version 0.4.6; Hartig F, 2022)
- wbCorr (version 0.1.22; Küng P, 2023)
- tibble (version 3.2.1; Müller K, Wickham H, 2023)
- R (version 4.4.1; R Core Team, 2024)
- openxlsx (version 4.2.7.1; Schauberger P, Walker A, 2024)
- ggplot2 (version 3.5.1; Wickham H, 2016)
- forcats (version 1.0.0; Wickham H, 2023)
- stringr (version 1.5.1; Wickham H, 2023)
- rvest (version 1.0.4; Wickham H, 2024)
- tidyverse (version 2.0.0; Wickham H et al., 2019)
- readxl (version 1.4.3; Wickham H, Bryan J, 2023)
- dplyr (version 1.1.4; Wickham H et al., 2023)
- purrr (version 1.0.2; Wickham H, Henry L, 2023)
- readr (version 2.1.5; Wickham H et al., 2024)
- xml2 (version 1.3.6; Wickham H et al., 2023)
- tidyr (version 1.3.1; Wickham H et al., 2024)
- knitr (version 1.48; Xie Y, 2024)
- kableExtra (version 1.4.0; Zhu H, 2024)
- Bååth R (2024). beepr: Easily Play Notification Sounds on any
Platform. R package version 2.0, https://CRAN.R-project.org/package=beepr.
- Bengtsson H (2003). “The R.oo package - Object-Oriented Programming
with References Using Standard R Code.” In Hornik K, Leisch F, Zeileis A
(eds.), Proceedings of the 3rd International Workshop on Distributed
Statistical Computing (DSC 2003). https://www.r-project.org/conferences/DSC-2003/Proceedings/Bengtsson.pdf.
- Bengtsson H (2003). “The R.oo package - Object-Oriented Programming
with References Using Standard R Code.” In Hornik K, Leisch F, Zeileis A
(eds.), Proceedings of the 3rd International Workshop on Distributed
Statistical Computing (DSC 2003). https://www.r-project.org/conferences/DSC-2003/Proceedings/Bengtsson.pdf.
- Bengtsson H (2023). R.utils: Various Programming Utilities.
R package version 2.12.3, https://CRAN.R-project.org/package=R.utils.
- Bürkner P (2017). “brms: An R Package for Bayesian Multilevel Models
Using Stan.” Journal of Statistical Software, 80(1),
1-28. doi:10.18637/jss.v080.i01 https://doi.org/10.18637/jss.v080.i01. Bürkner P (2018).
“Advanced Bayesian Multilevel Modeling with the R Package brms.” The
R Journal, 10(1), 395-411. doi:10.32614/RJ-2018-017
https://doi.org/10.32614/RJ-2018-017. Bürkner P (2021).
“Bayesian Item Response Modeling in R with brms and Stan.” Journal
of Statistical Software, 100(5), 1-54. doi:10.18637/jss.v100.i05 https://doi.org/10.18637/jss.v100.i05.
- Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N,
Ucar I, Bates D, Chambers J (2024). Rcpp: Seamless R and C++
Integration. R package version 1.0.13, https://CRAN.R-project.org/package=Rcpp. Eddelbuettel D,
François R (2011). “Rcpp: Seamless R and C++ Integration.” Journal
of Statistical Software, 40(8), 1-18. doi:10.18637/jss.v040.i08 https://doi.org/10.18637/jss.v040.i08. Eddelbuettel D
(2013). Seamless R and C++ Integration with Rcpp. Springer, New
York. doi:10.1007/978-1-4614-6868-4 https://doi.org/10.1007/978-1-4614-6868-4, ISBN
978-1-4614-6867-7. Eddelbuettel D, Balamuta J (2018). “Extending R with
C++: A Brief Introduction to Rcpp.” The American Statistician,
72(1), 28-36. doi:10.1080/00031305.2017.1375990 https://doi.org/10.1080/00031305.2017.1375990.
- Gabry J, Mahr T (2024). “bayesplot: Plotting for Bayesian Models.” R
package version 1.11.1, https://mc-stan.org/bayesplot/. Gabry J, Simpson D,
Vehtari A, Betancourt M, Gelman A (2019). “Visualization in Bayesian
workflow.” J. R. Stat. Soc. A, 182, 389-402. doi:10.1111/rssa.12378 https://doi.org/10.1111/rssa.12378.
- Grolemund G, Wickham H (2011). “Dates and Times Made Easy with
lubridate.” Journal of Statistical Software, 40(3),
1-25. https://www.jstatsoft.org/v40/i03/.
- Hartig F (2022). DHARMa: Residual Diagnostics for Hierarchical
(Multi-Level / Mixed) Regression Models. R package version 0.4.6,
https://CRAN.R-project.org/package=DHARMa.
- Küng P (2023). wbCorr: Bivariate Within- and Between-Cluster
Correlations. University of Zürich. R package version 0.1.22. https://github.com/Pascal-Kueng/wbCorr.
- Müller K, Wickham H (2023). tibble: Simple Data Frames. R
package version 3.2.1, https://CRAN.R-project.org/package=tibble.
- R Core Team (2024). R: A Language and Environment for
Statistical Computing. R Foundation for Statistical Computing,
Vienna, Austria. https://www.R-project.org/.
- Schauberger P, Walker A (2024). openxlsx: Read, Write and Edit
xlsx Files. R package version 4.2.7.1, https://CRAN.R-project.org/package=openxlsx.
- Wickham H (2016). ggplot2: Elegant Graphics for Data
Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
- Wickham H (2023). forcats: Tools for Working with Categorical
Variables (Factors). R package version 1.0.0, https://CRAN.R-project.org/package=forcats.
- Wickham H (2023). stringr: Simple, Consistent Wrappers for
Common String Operations. R package version 1.5.1, https://CRAN.R-project.org/package=stringr.
- Wickham H (2024). rvest: Easily Harvest (Scrape) Web Pages.
R package version 1.0.4, https://CRAN.R-project.org/package=rvest.
- Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R,
Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E,
Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K,
Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.”
Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686
https://doi.org/10.21105/joss.01686.
- Wickham H, Bryan J (2023). readxl: Read Excel Files. R
package version 1.4.3, https://CRAN.R-project.org/package=readxl.
- Wickham H, François R, Henry L, Müller K, Vaughan D (2023).
dplyr: A Grammar of Data Manipulation. R package version 1.1.4,
https://CRAN.R-project.org/package=dplyr.
- Wickham H, Henry L (2023). purrr: Functional Programming
Tools. R package version 1.0.2, https://CRAN.R-project.org/package=purrr.
- Wickham H, Hester J, Bryan J (2024). readr: Read Rectangular
Text Data. R package version 2.1.5, https://CRAN.R-project.org/package=readr.
- Wickham H, Hester J, Ooms J (2023). xml2: Parse XML. R
package version 1.3.6, https://CRAN.R-project.org/package=xml2.
- Wickham H, Vaughan D, Girlich M (2024). tidyr: Tidy Messy
Data. R package version 1.3.1, https://CRAN.R-project.org/package=tidyr.
- Xie Y (2024). knitr: A General-Purpose Package for Dynamic
Report Generation in R. R package version 1.48, https://yihui.org/knitr/. Xie Y (2015). Dynamic
Documents with R and knitr, 2nd edition. Chapman and Hall/CRC, Boca
Raton, Florida. ISBN 978-1498716963, https://yihui.org/knitr/. Xie Y (2014). “knitr: A
Comprehensive Tool for Reproducible Research in R.” In Stodden V, Leisch
F, Peng RD (eds.), Implementing Reproducible Computational
Research. Chapman and Hall/CRC. ISBN 978-1466561595.
- Zhu H (2024). kableExtra: Construct Complex Table with ‘kable’
and Pipe Syntax. R package version 1.4.0, https://CRAN.R-project.org/package=kableExtra.